UIST 2026
MolecularCanvas: LLM-assisted Small-Molecule Drug Discovery via Structure-Guided Constraints
* Equal contribution
I am currently a Statistics undergraduate at the University of Glasgow. My recent work combines interaction design and machine learning to build intelligent interactive systems that support multimodal intent expression in scientific workflows. I have also explored geometric approaches to graph representation learning.
My primary interests lie in human–AI interaction, LLM-assisted interfaces, and intelligent interactive systems. I am curious about how people communicate their ideas and intentions to AI, understand its responses, and stay involved as their ideas evolve. I would also like to explore interactive AI agents, as well as how wearable computing, extended reality (XR), and spatial interaction can support these experiences beyond the desktop.
Ultimately, I want to build systems that people can use in their everyday lives, taking research ideas beyond prototypes and turning them into tools that make a meaningful difference in how people interact with the world :)
I am currently seeking PhD opportunities in HCI, human–AI interaction, and related areas. If you think my interests might be a good fit for your group, I’d love to hear from you!
Selected publication
UIST 2026
* Equal contribution
Have a play with MolecularCanvas—edit a molecule and see how the pieces fit together.
UI demo · simulated resultsResearch experience
2025–2026Oct 2025 – Jul 2026
Lead Researcher · HKUST VisLab · Mentored by Dr. Yanna Lin
Led the interaction and system design of a human–AI molecular optimisation platform. Built the full-stack prototype and led expert studies with medicinal and computational chemists.
Mar 2026 – Jul 2026
Lead Researcher · Advised by Prof. Xuchu Jiang
Developed a plug-in GNN classifier with grouped prototypes in a learnable constant-curvature space, leading the method, implementation, experiments, and manuscript. Under review at ACM KDD 2027.
Feb 2025 – Aug 2025
Lead Researcher · Advised by Prof. Xuchu Jiang
Proposed a self-paced graph structure learning method with decaying masks, error-memory, and Gromov–Wasserstein global alignment. Manuscript under revision.
Education
2025–2027 expected
BSc (Hons) Statistics · Dual-Degree 2+2 Programme
Year 3 GPA: 18.8/22.0 · First-class level
2023–2025
BSc Statistics · Dual-Degree 2+2 Programme
Weighted average: 85/100
Teaching
2026–Present
University of Glasgow · Level 1 and Level 2 Statistics
Honors
Huazhong Cup Mathematical Modeling Challenge
Provincial Third Prize · Team Leader
China Undergraduate Mathematical Contest in Modeling
Provincial Third Prize · Team Leader